Simulation of radial oscillations of a spring-loaded roll in a roll compactor

Carried out simulation of oscillations of a spring-loaded roll in a roll compactor when interacting the powder being compacted with the rolls. Considering the separation of the feed and compaction areas in the contact area of the roll with the material being compacted, we obtain the dependence of the force acting on the roll on the gap size between the rolls. It is shown that this dependence is non-linear, and it can be described with a sufficiently high accuracy degree by an exponential function with a negative exponent in the working range. The given numerical solution of the equation of free nonlinear oscillations of the spring-loaded roll has shown that considering the deformation of the material being compacted leads to a reduction of the natural frequency of the system by 20–25 % compared to the case, where the pressure force of the powder on the roll is assumed to be independent of the gap size. The nonlinearity of the dependence of the pressure force on the gap also leads to the increase by 10 % in the calculated values of the maximum displacements. The developed approach to the calculation of oscillations of the spring-loaded roll in the roll compactor enables to take into account the peculiarities of deformation of the powder being compacted during its interaction with the rolls. In addition, it allows estimating the frequencies and oscillation amplitudes and setting the optimum range of spring rate values, at which the occurrence of resonance in the machine is not possible.

Bar-cas12a, a novel and rapid method for plant species authentication in case of Phyllanthus amarus Schumach. & Thonn

Rapid and accurate species diagnosis accelerates performance in numerous biological fields and associated areas. However, morphology-based species taxonomy/identification might hinder study and lead to ambiguous results. DNA barcodes (Bar) has been employed extensively for plant species identification. Recently, CRISPR-cas system can be applied for diagnostic tool to detect pathogen’s DNA based on the collateral activity of cas12a or cas13. Here, we developed barcode-coupled with cas12a assay, “Bar-cas12a” for species authentication using Phyllanthus amarus as a model. The gRNAs were designed from trnL region, namely gRNA-A and gRNA-B. As a result, gRNA-A was highly specific to P. amarus amplified by RPA in contrast to gRNA-B even in contaminated condition. Apart from the large variation of gRNA-A binding in DNA target, cas12a- specific PAM’s gRNA-A as TTTN can be found only in P. amarus. PAM site may be recognized one of the potential regions for increasing specificity to authenticate species. In addition, the sensitivity of Bar-cas12a using both gRNAs gave the same detection limit at 0.8 fg and it was 1,000 times more sensitive compared to agarose gel electrophoresis. This approach displayed the accuracy degree of 90% for species authentication. Overall, Bar-cas12a using trnL-designed gRNA offer a highly specific, sensitive, speed, and simple approach for plant species authentication. Therefore, the current method serves as a promising tool for species determination which is likely to be implemented for onsite testing.

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

A New Artificial Neural Network Based Failure Determination System For Electric Motors

A new measurement system was developed for determination of failures and defining the level of failure that may occur in bearings and rotor bearings or in foot of motor in single phase capacitor start motor. In system, the vibratory operation of the motor is provided by connecting different screws on the motor’s rotor mounted flywheel or by gradually removing the nut bolts of motor foot. The VB3 vibration sensor outputs were recorded to the computer. The changing characteristics of sensor output for each experiment had more than one frequency component; therefore, FFT was performed for determining such components. It was observed that the frequency and amplitude values of first 5 harmonics could be used for determining the presence, type and level of failure but there was a nonlinear relation between each other. 2 different ANN customized separately were developed for determining the type and rate of the failure of motor. 80%, 10% and 10% of available data were reserved for training, testing and verification respectively and the ANN was trained. Accuracy degree for the ANN in the estimations following the training stage was calculated as R = 0.97-0.98. Furthermore, the results of ANN was compared with the results obtained using Sequential Minimal Optimization (SMO), Naive Bayes (NB) and J48 algorithms; and it was determined that the accuracy degree of ANN was higher.

Single-Valued Neutrosophic Set Correlation Coefficient and Its Application in Fault Diagnosis

With the increasing automation of mechanical equipment, fault diagnosis becomes more and more important. However, the factors that cause mechanical failures are becoming more and more complex, and the uncertainty and coupling between the factors are getting higher and higher. In order to solve the given problem, this paper proposes a single-valued neutrosophic set ISVNS algorithm for processing of uncertain and inaccurate information in fault diagnosis, which generates neutrosophic set by triangular fuzzy number and introduces the formula of the improved weighted correlation coefficient. Since both the single-valued neutrosophic set data and the ideal neutrosophic set data are considered, the proposed method solves the fault diagnosis problem more effectively. Finally, experiments show that the algorithm can significantly improve the accuracy degree of fault diagnosis, and can better satisfy the diagnostic requirements in practice.

Handwriting Character Recognition using Vector Quantization Technique

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.

Steady-State Performance Analysis of Tracking Filter Using LFM Waveforms and Range-Rate Measurement

The steady-state performance of a moving-object tracking filter is theoretically analyzed, assuming the simultaneous measurement of the range and range-rate (RRM system), and the use of linear frequency modulated (LFM) waveforms (RRM-LFM filter). An efficient analytical steady-state performance index, called an RMS index, is derived for the RRM-LFM filter to clarify the steady-state range prediction errors, theoretically. Using the derived RMS index, the optimal performance of the RRM-LFM filter is analyzed. The performance variation due to the use of LFM waveforms is clarified for the RRM tracking system. The theoretical performance analysis verifies that the measured range-rate significantly improves the tracking accuracy, compared to the conventional range-only measuring LFM tracking filter. Furthermore, the quantitative relationships among the measurement accuracy, degree of target maneuvering, and steady-state range prediction errors are clarified to validate the effectiveness of the RRM-LFM filter.

Peculiarities of Movement Plastique Development in Young Gymnasts

The purpose of this study was to substantiate and create a technique for the development of movement plastique in gymnastics at the initial training stage and experimentally verify its effectiveness. Materials and methods: The study participants were 28 boys aged 6-7 engaged in gymnastics (14 – control group and 14 – experimental group). To achieve the purpose set, the study used the following research methods: analysis of scientific and methodological literature, pedagogical testing and methods of mathematical statistics. Results: The use of a special technique that includes exercises developing expressive movements, exercises of classical and parterre choreography, rhythmic gymnastics and elementary dance exercises at the initial training stage in gymnastics improved the development level of movement plastique and its individual components, in particular the amplitude and dynamism (at p<0.05), the accuracy and the degree of using accompanying movements (at p<0.001). The study revealed strong and average correlations between the individual indicators and movement plastique in general. Conclusion: The study defined the criteria and modern requirements for gymnasts’ movement plastique, as well as its individual indicators: amplitude, accuracy, degree of using accompanying movements, and dynamism. The study created a technique for the development of movement plastique in young gymnasts and experimentally proved its effectiveness.

Classification Technique of Interviewer-Bot Result using Naïve Bayes and Phrase Reinforcement Algorithms

Students with hectic college schedules tend not to have enough time repeating the course material. Meanwhile, after they graduated, to be accepted in a foreign company with a higher salary, they must be ready for the English-based interview. To meet these needs, they try to practice conversing with someone who is proficient in English. On the other hand, it is not easy to have someone who is not only proficient in English, but also understand about a job interview related topics. This paper presents the development of a machine which is able to provide practice on English-based interviews, specifically on job interviews. Interviewer machine (interviewer bot) is expected to help students practice on speaking English in particular issue of finding suitable job. The interviewer machine design uses words from a chat bot database named ALICE to mimic human intelligence that can be applied to a search engine using AIML. Naïve Bayes algorithm is used to classify the interview results into three categories: POTENTIAL, TALENT and INTEREST students. Furthermore, based on the classification result, the summary is made at the end of the interview session by using phrase reinforcement algorithms. By using this bot, students are expected to practice their listening and speaking skills, also to be familiar with the questions often asked in job interviews so that they can prepare the proper answers. In addition, the bot’ users could know their potential, talent and interest in finding a job, so they could apply to the appropriate companies. Based on the validation results of 50 respondents, the accuracy degree of interviewer chat-bot (interviewer engine) response obtained 86.93%.

Evaluating Machine Translations from Arabic into English and Vice Versa

Abstract—Machine translation (MT) allows direct communication between two persons without the need for the third party or via dictionary in your pocket, which could bring significant and per formative improvement. Since most traditional translational way is a word-sensitive, it is very important to consider the word order in addition to word selection in the evaluation of any machine translation. To evaluate the MT performance, it is necessary to dynamically observe the translation in the machine translator tool according to word order, and word selection and furthermore the sentence length. However, applying a good evaluation with respect to all previous points is a very challenging issue. In this paper, we first summarize various approaches to evaluate machine translation. We propose a practical solution by selecting an appropriate powerful tool called iBLEU to evaluate the accuracy degree of famous MT tools (i.e. Google, Bing, Systranet and Babylon). Based on the solution structure, we further discuss the performance order for these tools in both directions Arabic to English and English to Arabic. After extensive testing, we can decide that any direction gives more accurate results in translation based on the selected machine translations MTs. Finally, we proved the choosing of Google as best system performance and Systranet as the worst one. Index Terms: Machine Translation, MTs, Evaluation for Machine Translation, Google, Bing, Systranet and Babylon, Machine Translation tools, BLEU, iBLEU.